Overview

Brought to you by YData

Dataset statistics

Number of variables14
Number of observations750
Missing cells0
Missing cells (%)0.0%
Duplicate rows12
Duplicate rows (%)1.6%
Total size in memory82.2 KiB
Average record size in memory112.2 B

Variable types

Numeric11
Categorical3

Alerts

Dataset has 12 (1.6%) duplicate rowsDuplicates
acousticness is highly overall correlated with energy and 1 other fieldsHigh correlation
energy is highly overall correlated with acousticness and 1 other fieldsHigh correlation
loudness is highly overall correlated with acousticness and 1 other fieldsHigh correlation
time_signature is highly imbalanced (71.1%)Imbalance
instrumentalness has 290 (38.7%) zerosZeros
key has 113 (15.1%) zerosZeros

Reproduction

Analysis started2024-08-12 21:55:38.159180
Analysis finished2024-08-12 21:56:40.348407
Duration1 minute and 2.19 seconds
Software versionydata-profiling v0.0.dev0
Download configurationconfig.json

Variables

acousticness
Real number (ℝ)

HIGH CORRELATION 

Distinct596
Distinct (%)79.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.35739417
Minimum1.17 × 10-6
Maximum0.994
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.0 KiB
2024-08-12T18:56:40.859643image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum1.17 × 10-6
5-th percentile0.001189
Q10.03715
median0.2445
Q30.6785
95-th percentile0.95155
Maximum0.994
Range0.99399883
Interquartile range (IQR)0.64135

Descriptive statistics

Standard deviation0.33840528
Coefficient of variation (CV)0.94686848
Kurtosis-1.2102957
Mean0.35739417
Median Absolute Deviation (MAD)0.2328
Skewness0.53480424
Sum268.04563
Variance0.11451813
MonotonicityNot monotonic
2024-08-12T18:56:41.457365image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.992 7
 
0.9%
0.99 5
 
0.7%
0.586 4
 
0.5%
0.741 4
 
0.5%
0.194 4
 
0.5%
0.713 3
 
0.4%
0.541 3
 
0.4%
0.284 3
 
0.4%
0.22 3
 
0.4%
0.11 3
 
0.4%
Other values (586) 711
94.8%
ValueCountFrequency (%)
1.17 × 10-61
0.1%
2.04 × 10-61
0.1%
2.74 × 10-61
0.1%
2.8 × 10-61
0.1%
3.68 × 10-61
0.1%
3.11 × 10-51
0.1%
6.34 × 10-51
0.1%
6.64 × 10-51
0.1%
9.16 × 10-51
0.1%
0.000107 1
0.1%
ValueCountFrequency (%)
0.994 3
0.4%
0.993 2
 
0.3%
0.992 7
0.9%
0.991 1
 
0.1%
0.99 5
0.7%
0.988 1
 
0.1%
0.987 1
 
0.1%
0.984 1
 
0.1%
0.983 2
 
0.3%
0.982 1
 
0.1%

danceability
Real number (ℝ)

Distinct458
Distinct (%)61.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.59643867
Minimum0.107
Maximum0.986
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.0 KiB
2024-08-12T18:56:41.897224image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0.107
5-th percentile0.2938
Q10.48
median0.606
Q30.71575
95-th percentile0.85655
Maximum0.986
Range0.879
Interquartile range (IQR)0.23575

Descriptive statistics

Standard deviation0.17203641
Coefficient of variation (CV)0.2884394
Kurtosis-0.29670569
Mean0.59643867
Median Absolute Deviation (MAD)0.117
Skewness-0.31198091
Sum447.329
Variance0.029596527
MonotonicityNot monotonic
2024-08-12T18:56:42.393465image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.525 6
 
0.8%
0.646 6
 
0.8%
0.685 5
 
0.7%
0.531 5
 
0.7%
0.578 5
 
0.7%
0.401 5
 
0.7%
0.541 5
 
0.7%
0.708 5
 
0.7%
0.836 4
 
0.5%
0.438 4
 
0.5%
Other values (448) 700
93.3%
ValueCountFrequency (%)
0.107 1
0.1%
0.135 1
0.1%
0.157 1
0.1%
0.158 1
0.1%
0.167 1
0.1%
0.173 1
0.1%
0.181 1
0.1%
0.183 1
0.1%
0.185 1
0.1%
0.187 1
0.1%
ValueCountFrequency (%)
0.986 1
0.1%
0.981 1
0.1%
0.978 1
0.1%
0.976 2
0.3%
0.97 1
0.1%
0.96 1
0.1%
0.942 1
0.1%
0.923 1
0.1%
0.919 1
0.1%
0.916 1
0.1%

duration
Real number (ℝ)

Distinct717
Distinct (%)95.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean220112.73
Minimum33840
Maximum675360
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.0 KiB
2024-08-12T18:56:43.047317image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum33840
5-th percentile123523.6
Q1185490.25
median215108.5
Q3244236.75
95-th percentile324082.15
Maximum675360
Range641520
Interquartile range (IQR)58746.5

Descriptive statistics

Standard deviation65587.69
Coefficient of variation (CV)0.29797318
Kurtosis8.3218632
Mean220112.73
Median Absolute Deviation (MAD)29348.5
Skewness1.5762311
Sum1.6508455 × 108
Variance4.3017451 × 109
MonotonicityNot monotonic
2024-08-12T18:56:43.711329image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
238933 3
 
0.4%
213440 3
 
0.4%
228253 3
 
0.4%
248560 2
 
0.3%
190000 2
 
0.3%
174785 2
 
0.3%
188013 2
 
0.3%
249493 2
 
0.3%
272333 2
 
0.3%
292093 2
 
0.3%
Other values (707) 727
96.9%
ValueCountFrequency (%)
33840 1
0.1%
46107 1
0.1%
48093 1
0.1%
55653 1
0.1%
56331 1
0.1%
58671 1
0.1%
62622 1
0.1%
66481 1
0.1%
67213 1
0.1%
70352 1
0.1%
ValueCountFrequency (%)
675360 1
0.1%
618400 1
0.1%
592000 1
0.1%
547880 1
0.1%
538800 1
0.1%
520661 1
0.1%
483667 1
0.1%
482333 1
0.1%
411520 1
0.1%
403280 1
0.1%

energy
Real number (ℝ)

HIGH CORRELATION 

Distinct502
Distinct (%)66.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.59418793
Minimum0.00925
Maximum0.995
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.0 KiB
2024-08-12T18:56:44.590906image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0.00925
5-th percentile0.1309
Q10.42325
median0.6315
Q30.80475
95-th percentile0.9441
Maximum0.995
Range0.98575
Interquartile range (IQR)0.3815

Descriptive statistics

Standard deviation0.2533013
Coefficient of variation (CV)0.4262983
Kurtosis-0.75896176
Mean0.59418793
Median Absolute Deviation (MAD)0.188
Skewness-0.4587646
Sum445.64095
Variance0.06416155
MonotonicityNot monotonic
2024-08-12T18:56:45.623585image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.948 5
 
0.7%
0.854 5
 
0.7%
0.771 5
 
0.7%
0.832 5
 
0.7%
0.666 5
 
0.7%
0.868 4
 
0.5%
0.457 4
 
0.5%
0.543 4
 
0.5%
0.6 4
 
0.5%
0.857 4
 
0.5%
Other values (492) 705
94.0%
ValueCountFrequency (%)
0.00925 1
0.1%
0.0153 1
0.1%
0.0156 1
0.1%
0.0198 1
0.1%
0.0276 1
0.1%
0.0305 1
0.1%
0.0343 1
0.1%
0.0381 1
0.1%
0.0416 1
0.1%
0.0423 1
0.1%
ValueCountFrequency (%)
0.995 2
0.3%
0.992 1
0.1%
0.991 1
0.1%
0.99 1
0.1%
0.989 1
0.1%
0.988 1
0.1%
0.987 1
0.1%
0.982 1
0.1%
0.979 1
0.1%
0.977 1
0.1%

instrumentalness
Real number (ℝ)

ZEROS 

Distinct431
Distinct (%)57.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.10024481
Minimum0
Maximum0.967
Zeros290
Zeros (%)38.7%
Negative0
Negative (%)0.0%
Memory size6.0 KiB
2024-08-12T18:56:46.841709image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1.02 × 10-5
Q30.002245
95-th percentile0.872
Maximum0.967
Range0.967
Interquartile range (IQR)0.002245

Descriptive statistics

Standard deviation0.25992145
Coefficient of variation (CV)2.5928668
Kurtosis4.5180124
Mean0.10024481
Median Absolute Deviation (MAD)1.02 × 10-5
Skewness2.4881657
Sum75.18361
Variance0.067559163
MonotonicityNot monotonic
2024-08-12T18:56:48.297266image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 290
38.7%
0.872 3
 
0.4%
1.02 × 10-53
 
0.4%
0.000229 3
 
0.4%
0.000623 2
 
0.3%
1.9 × 10-62
 
0.3%
0.908 2
 
0.3%
0.0159 2
 
0.3%
0.93 2
 
0.3%
0.000156 2
 
0.3%
Other values (421) 439
58.5%
ValueCountFrequency (%)
0 290
38.7%
1.06 × 10-61
 
0.1%
1.07 × 10-61
 
0.1%
1.11 × 10-61
 
0.1%
1.13 × 10-61
 
0.1%
1.17 × 10-61
 
0.1%
1.21 × 10-61
 
0.1%
1.23 × 10-61
 
0.1%
1.34 × 10-61
 
0.1%
1.38 × 10-61
 
0.1%
ValueCountFrequency (%)
0.967 1
0.1%
0.956 1
0.1%
0.949 1
0.1%
0.947 1
0.1%
0.94 1
0.1%
0.937 1
0.1%
0.934 1
0.1%
0.933 2
0.3%
0.931 1
0.1%
0.93 2
0.3%

key
Real number (ℝ)

ZEROS 

Distinct12
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.8293333
Minimum0
Maximum11
Zeros113
Zeros (%)15.1%
Negative0
Negative (%)0.0%
Memory size6.0 KiB
2024-08-12T18:56:49.545604image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median5
Q38
95-th percentile11
Maximum11
Range11
Interquartile range (IQR)7

Descriptive statistics

Standard deviation3.6360006
Coefficient of variation (CV)0.75289907
Kurtosis-1.3497295
Mean4.8293333
Median Absolute Deviation (MAD)3
Skewness0.15143135
Sum3622
Variance13.2205
MonotonicityNot monotonic
2024-08-12T18:56:50.713338image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
0 113
15.1%
2 92
12.3%
1 85
11.3%
9 74
9.9%
5 69
9.2%
7 63
8.4%
8 51
6.8%
4 48
6.4%
10 48
6.4%
11 46
6.1%
Other values (2) 61
8.1%
ValueCountFrequency (%)
0 113
15.1%
1 85
11.3%
2 92
12.3%
3 17
 
2.3%
4 48
6.4%
5 69
9.2%
6 44
 
5.9%
7 63
8.4%
8 51
6.8%
9 74
9.9%
ValueCountFrequency (%)
11 46
6.1%
10 48
6.4%
9 74
9.9%
8 51
6.8%
7 63
8.4%
6 44
5.9%
5 69
9.2%
4 48
6.4%
3 17
 
2.3%
2 92
12.3%

liveness
Real number (ℝ)

Distinct445
Distinct (%)59.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.20337613
Minimum0.024
Maximum0.979
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.0 KiB
2024-08-12T18:56:52.539391image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0.024
5-th percentile0.06097
Q10.09455
median0.129
Q30.26475
95-th percentile0.62475
Maximum0.979
Range0.955
Interquartile range (IQR)0.1702

Descriptive statistics

Standard deviation0.17760862
Coefficient of variation (CV)0.87330117
Kurtosis4.8427012
Mean0.20337613
Median Absolute Deviation (MAD)0.05055
Skewness2.1562405
Sum152.5321
Variance0.03154482
MonotonicityNot monotonic
2024-08-12T18:56:53.761421image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.102 12
 
1.6%
0.111 11
 
1.5%
0.122 10
 
1.3%
0.106 8
 
1.1%
0.101 8
 
1.1%
0.108 8
 
1.1%
0.107 8
 
1.1%
0.103 7
 
0.9%
0.131 7
 
0.9%
0.113 7
 
0.9%
Other values (435) 664
88.5%
ValueCountFrequency (%)
0.024 1
0.1%
0.0246 1
0.1%
0.0277 1
0.1%
0.0309 1
0.1%
0.0348 1
0.1%
0.0365 1
0.1%
0.0373 1
0.1%
0.0388 1
0.1%
0.0407 1
0.1%
0.045 1
0.1%
ValueCountFrequency (%)
0.979 1
0.1%
0.97 1
0.1%
0.966 1
0.1%
0.931 1
0.1%
0.924 1
0.1%
0.922 2
0.3%
0.905 1
0.1%
0.875 1
0.1%
0.874 1
0.1%
0.873 1
0.1%

loudness
Real number (ℝ)

HIGH CORRELATION 

Distinct710
Distinct (%)94.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-8.5093387
Minimum-29.601
Maximum-0.533
Zeros0
Zeros (%)0.0%
Negative750
Negative (%)100.0%
Memory size6.0 KiB
2024-08-12T18:56:54.856033image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum-29.601
5-th percentile-19.68325
Q1-10.1735
median-7.27
Q3-5.09775
95-th percentile-3.0578
Maximum-0.533
Range29.068
Interquartile range (IQR)5.07575

Descriptive statistics

Standard deviation5.0394884
Coefficient of variation (CV)-0.59223033
Kurtosis3.2329168
Mean-8.5093387
Median Absolute Deviation (MAD)2.4685
Skewness-1.6931147
Sum-6382.004
Variance25.396444
MonotonicityNot monotonic
2024-08-12T18:56:55.916955image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-7.042 3
 
0.4%
-6.699 3
 
0.4%
-7.273 3
 
0.4%
-5.436 2
 
0.3%
-2.597 2
 
0.3%
-5.805 2
 
0.3%
-5.853 2
 
0.3%
-7.408 2
 
0.3%
-6.196 2
 
0.3%
-8.559 2
 
0.3%
Other values (700) 727
96.9%
ValueCountFrequency (%)
-29.601 1
0.1%
-28.841 1
0.1%
-28.55 1
0.1%
-27.817 1
0.1%
-27.473 1
0.1%
-27.456 1
0.1%
-27.421 1
0.1%
-27.385 1
0.1%
-27.127 1
0.1%
-26.586 1
0.1%
ValueCountFrequency (%)
-0.533 1
0.1%
-0.913 1
0.1%
-1.319 1
0.1%
-1.455 1
0.1%
-1.737 1
0.1%
-2.024 1
0.1%
-2.097 1
0.1%
-2.204 1
0.1%
-2.218 1
0.1%
-2.274 1
0.1%

mode
Categorical

Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size6.0 KiB
1
556 
0
194 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters750
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row1
4th row1
5th row0

Common Values

ValueCountFrequency (%)
1 556
74.1%
0 194
 
25.9%

Length

2024-08-12T18:56:56.913916image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-08-12T18:56:57.644415image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
1 556
74.1%
0 194
 
25.9%

Most occurring characters

ValueCountFrequency (%)
1 556
74.1%
0 194
 
25.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 750
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 556
74.1%
0 194
 
25.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 750
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 556
74.1%
0 194
 
25.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 750
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 556
74.1%
0 194
 
25.9%

speechiness
Real number (ℝ)

Distinct457
Distinct (%)60.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.098966267
Minimum0.0234
Maximum0.721
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.0 KiB
2024-08-12T18:56:58.435601image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0.0234
5-th percentile0.028345
Q10.0359
median0.04875
Q30.113
95-th percentile0.33655
Maximum0.721
Range0.6976
Interquartile range (IQR)0.0771

Descriptive statistics

Standard deviation0.10471452
Coefficient of variation (CV)1.0580829
Kurtosis4.149336
Mean0.098966267
Median Absolute Deviation (MAD)0.01725
Skewness2.0403698
Sum74.2247
Variance0.01096513
MonotonicityNot monotonic
2024-08-12T18:56:59.030959image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0349 8
 
1.1%
0.0291 5
 
0.7%
0.0289 5
 
0.7%
0.0297 5
 
0.7%
0.0278 5
 
0.7%
0.0407 5
 
0.7%
0.105 5
 
0.7%
0.0417 5
 
0.7%
0.0362 5
 
0.7%
0.0296 4
 
0.5%
Other values (447) 698
93.1%
ValueCountFrequency (%)
0.0234 1
0.1%
0.0237 1
0.1%
0.024 1
0.1%
0.0243 1
0.1%
0.0248 1
0.1%
0.0256 1
0.1%
0.0257 2
0.3%
0.0259 1
0.1%
0.026 1
0.1%
0.0261 2
0.3%
ValueCountFrequency (%)
0.721 1
0.1%
0.57 1
0.1%
0.567 1
0.1%
0.511 1
0.1%
0.458 2
0.3%
0.447 1
0.1%
0.446 1
0.1%
0.438 1
0.1%
0.431 1
0.1%
0.429 1
0.1%

tempo
Real number (ℝ)

Distinct729
Distinct (%)97.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean120.40576
Minimum55.747
Maximum204.162
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.0 KiB
2024-08-12T18:56:59.561268image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum55.747
5-th percentile78.54465
Q198.998
median120.1045
Q3138.07475
95-th percentile171.028
Maximum204.162
Range148.415
Interquartile range (IQR)39.07675

Descriptive statistics

Standard deviation28.378116
Coefficient of variation (CV)0.23568736
Kurtosis-0.2461656
Mean120.40576
Median Absolute Deviation (MAD)19.906
Skewness0.33476254
Sum90304.321
Variance805.31749
MonotonicityNot monotonic
2024-08-12T18:57:00.199181image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
157.894 3
 
0.4%
89.019 3
 
0.4%
87.02 2
 
0.3%
109.394 2
 
0.3%
130.089 2
 
0.3%
163.987 2
 
0.3%
127.992 2
 
0.3%
104.999 2
 
0.3%
122.021 2
 
0.3%
100.996 2
 
0.3%
Other values (719) 728
97.1%
ValueCountFrequency (%)
55.747 1
0.1%
59.896 1
0.1%
64.128 1
0.1%
64.572 1
0.1%
64.773 1
0.1%
64.983 1
0.1%
65.09 2
0.3%
66.47 1
0.1%
67.325 2
0.3%
69.321 1
0.1%
ValueCountFrequency (%)
204.162 1
0.1%
203.988 1
0.1%
203.927 1
0.1%
203.669 1
0.1%
201.843 1
0.1%
201.8 1
0.1%
199.512 1
0.1%
191.287 1
0.1%
188.055 1
0.1%
187.451 1
0.1%

time_signature
Categorical

IMBALANCE 

Distinct4
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size6.0 KiB
4
671 
3
 
64
5
 
9
1
 
6

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters750
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row4
2nd row4
3rd row4
4th row4
5th row4

Common Values

ValueCountFrequency (%)
4 671
89.5%
3 64
 
8.5%
5 9
 
1.2%
1 6
 
0.8%

Length

2024-08-12T18:57:00.760456image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-08-12T18:57:01.154523image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
4 671
89.5%
3 64
 
8.5%
5 9
 
1.2%
1 6
 
0.8%

Most occurring characters

ValueCountFrequency (%)
4 671
89.5%
3 64
 
8.5%
5 9
 
1.2%
1 6
 
0.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 750
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
4 671
89.5%
3 64
 
8.5%
5 9
 
1.2%
1 6
 
0.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 750
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
4 671
89.5%
3 64
 
8.5%
5 9
 
1.2%
1 6
 
0.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 750
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
4 671
89.5%
3 64
 
8.5%
5 9
 
1.2%
1 6
 
0.8%

valence
Real number (ℝ)

Distinct487
Distinct (%)64.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.49732147
Minimum0.0332
Maximum0.975
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.0 KiB
2024-08-12T18:57:01.662066image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0.0332
5-th percentile0.12745
Q10.297
median0.483
Q30.6845
95-th percentile0.904
Maximum0.975
Range0.9418
Interquartile range (IQR)0.3875

Descriptive statistics

Standard deviation0.2396149
Coefficient of variation (CV)0.48181089
Kurtosis-0.91100701
Mean0.49732147
Median Absolute Deviation (MAD)0.192
Skewness0.1048116
Sum372.9911
Variance0.057415301
MonotonicityNot monotonic
2024-08-12T18:57:02.343303image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.199 5
 
0.7%
0.668 5
 
0.7%
0.541 4
 
0.5%
0.261 4
 
0.5%
0.438 4
 
0.5%
0.622 4
 
0.5%
0.29 4
 
0.5%
0.249 4
 
0.5%
0.966 4
 
0.5%
0.403 4
 
0.5%
Other values (477) 708
94.4%
ValueCountFrequency (%)
0.0332 1
0.1%
0.0357 1
0.1%
0.036 1
0.1%
0.0374 1
0.1%
0.0375 1
0.1%
0.0381 1
0.1%
0.0388 1
0.1%
0.0389 1
0.1%
0.04 1
0.1%
0.0416 1
0.1%
ValueCountFrequency (%)
0.975 1
 
0.1%
0.968 2
0.3%
0.967 1
 
0.1%
0.966 4
0.5%
0.965 2
0.3%
0.964 1
 
0.1%
0.963 1
 
0.1%
0.962 2
0.3%
0.961 1
 
0.1%
0.959 1
 
0.1%

label
Categorical

Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size6.0 KiB
1
452 
0
298 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters750
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 452
60.3%
0 298
39.7%

Length

2024-08-12T18:57:02.889595image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-08-12T18:57:03.492636image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
1 452
60.3%
0 298
39.7%

Most occurring characters

ValueCountFrequency (%)
1 452
60.3%
0 298
39.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 750
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 452
60.3%
0 298
39.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 750
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 452
60.3%
0 298
39.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 750
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 452
60.3%
0 298
39.7%

Interactions

2024-08-12T18:56:31.493670image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-08-12T18:55:39.867607image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-08-12T18:55:45.104973image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-08-12T18:55:49.567264image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-08-12T18:55:53.876145image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-08-12T18:55:58.210321image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-08-12T18:56:03.382403image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-08-12T18:56:07.599557image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-08-12T18:56:11.686186image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-08-12T18:56:17.055867image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-08-12T18:56:22.632847image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-08-12T18:56:32.157233image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-08-12T18:55:40.401994image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-08-12T18:55:45.463243image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-08-12T18:55:49.939027image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-08-12T18:55:54.293940image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-08-12T18:55:58.636315image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-08-12T18:56:03.811495image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-08-12T18:56:07.906105image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-08-12T18:56:12.441805image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-08-12T18:56:17.467118image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-08-12T18:56:23.089944image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-08-12T18:56:32.803267image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-08-12T18:55:40.834364image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-08-12T18:55:45.814623image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-08-12T18:55:50.341944image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-08-12T18:55:54.658832image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-08-12T18:55:59.111751image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-08-12T18:56:04.165546image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-08-12T18:56:08.233398image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-08-12T18:56:12.997746image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-08-12T18:56:17.831256image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-08-12T18:56:23.481358image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-08-12T18:56:33.489393image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-08-12T18:55:41.287097image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-08-12T18:55:46.214874image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-08-12T18:55:50.793225image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-08-12T18:55:55.057613image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-08-12T18:55:59.803400image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-08-12T18:56:04.587526image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-08-12T18:56:08.659317image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-08-12T18:56:13.532835image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-08-12T18:56:18.327649image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-08-12T18:56:23.868956image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-08-12T18:56:34.178924image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-08-12T18:55:41.651726image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-08-12T18:55:46.611145image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-08-12T18:55:51.145218image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-08-12T18:55:55.377196image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-08-12T18:56:00.222598image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-08-12T18:56:04.960949image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-08-12T18:56:09.055767image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-08-12T18:56:13.952682image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-08-12T18:56:18.805065image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-08-12T18:56:24.514468image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-08-12T18:56:34.831707image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-08-12T18:55:42.035352image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-08-12T18:55:47.004248image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-08-12T18:55:51.492477image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-08-12T18:55:55.716384image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-08-12T18:56:00.689536image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-08-12T18:56:05.347952image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-08-12T18:56:09.436057image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-08-12T18:56:14.352675image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-08-12T18:56:19.352466image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-08-12T18:56:25.027798image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-08-12T18:56:35.406147image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-08-12T18:55:42.431980image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-08-12T18:55:47.364648image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-08-12T18:55:51.829532image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-08-12T18:55:56.107532image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-08-12T18:56:01.169927image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-08-12T18:56:05.665886image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-08-12T18:56:09.785193image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-08-12T18:56:14.783111image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-08-12T18:56:19.831828image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-08-12T18:56:25.949081image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-08-12T18:56:35.943973image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-08-12T18:55:42.895833image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-08-12T18:55:47.675532image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-08-12T18:55:52.200399image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-08-12T18:55:56.551821image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-08-12T18:56:01.583498image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-08-12T18:56:06.040685image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-08-12T18:56:10.139817image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-08-12T18:56:15.208075image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-08-12T18:56:20.503058image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-08-12T18:56:27.199797image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-08-12T18:56:36.391878image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-08-12T18:55:43.353377image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-08-12T18:55:48.096692image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-08-12T18:55:52.578840image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-08-12T18:55:56.976969image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-08-12T18:56:01.981161image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-08-12T18:56:06.451019image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-08-12T18:56:10.550990image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-08-12T18:56:15.630838image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-08-12T18:56:21.024606image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-08-12T18:56:28.221930image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-08-12T18:56:36.935553image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-08-12T18:55:44.145944image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-08-12T18:55:48.711347image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-08-12T18:55:53.069928image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-08-12T18:55:57.402951image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-08-12T18:56:02.483837image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-08-12T18:56:06.818997image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-08-12T18:56:10.940207image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-08-12T18:56:16.137630image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-08-12T18:56:21.584812image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-08-12T18:56:29.351717image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-08-12T18:56:37.360431image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-08-12T18:55:44.632822image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-08-12T18:55:49.215927image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-08-12T18:55:53.475576image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-08-12T18:55:57.804744image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-08-12T18:56:02.956941image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-08-12T18:56:07.262657image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-08-12T18:56:11.330217image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-08-12T18:56:16.616034image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-08-12T18:56:22.093840image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-08-12T18:56:30.555506image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Correlations

2024-08-12T18:57:03.830953image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
acousticnessdanceabilitydurationenergyinstrumentalnesskeylabellivenessloudnessmodespeechinesstempotime_signaturevalence
acousticness1.000-0.3630.004-0.7430.086-0.0520.481-0.149-0.6860.129-0.271-0.1550.182-0.157
danceability-0.3631.000-0.1860.315-0.1120.0540.387-0.1090.3400.0180.3360.0350.1860.463
duration0.004-0.1861.000-0.0540.1760.0040.158-0.067-0.1090.000-0.149-0.0230.037-0.218
energy-0.7430.315-0.0541.000-0.0840.0620.4620.2510.8480.1470.2650.2000.1760.347
instrumentalness0.086-0.1120.176-0.0841.0000.0080.116-0.061-0.2060.007-0.165-0.0650.000-0.168
key-0.0520.0540.0040.0620.0081.0000.132-0.0550.0270.2960.065-0.0880.0620.069
label0.4810.3870.1580.4620.1160.1321.0000.2120.4770.0680.5000.1450.1540.155
liveness-0.149-0.109-0.0670.251-0.061-0.0550.2121.0000.1870.0000.0900.0400.0000.077
loudness-0.6860.340-0.1090.848-0.2060.0270.4770.1871.0000.0710.2740.1490.1530.260
mode0.1290.0180.0000.1470.0070.2960.0680.0000.0711.0000.1230.0000.0000.032
speechiness-0.2710.336-0.1490.265-0.1650.0650.5000.0900.2740.1231.0000.1230.0340.133
tempo-0.1550.035-0.0230.200-0.065-0.0880.1450.0400.1490.0000.1231.0000.1400.071
time_signature0.1820.1860.0370.1760.0000.0620.1540.0000.1530.0000.0340.1401.0000.063
valence-0.1570.463-0.2180.347-0.1680.0690.1550.0770.2600.0320.1330.0710.0631.000

Missing values

2024-08-12T18:56:38.439381image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
A simple visualization of nullity by column.
2024-08-12T18:56:39.825085image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

acousticnessdanceabilitydurationenergyinstrumentalnesskeylivenessloudnessmodespeechinesstempotime_signaturevalencelabel
00.7130.5141001250.5210.81600080.1120-14.83500.0444119.87940.1431
10.1920.7142070190.6140.00000040.2630-6.93510.0319123.96940.5821
20.3330.6302162000.4550.00000450.1270-9.29010.0292139.93140.1991
30.6010.8101364130.2210.21000050.1840-11.00510.0429109.96040.7981
40.8830.4651814400.4590.00017360.0692-8.13700.035190.80740.2881
50.5240.6332443600.4010.00000040.1230-12.54910.0439134.97840.5231
60.5970.5071835730.7950.00000090.2960-6.96610.0607165.54040.9000
70.4520.8252591020.4350.60900010.0953-9.58210.0568119.03840.2431
80.7480.4203661790.3240.83900090.0723-14.70000.0556183.02030.3301
90.9130.2921976130.2460.08830000.2090-9.75810.0330140.31640.2491
acousticnessdanceabilitydurationenergyinstrumentalnesskeylivenessloudnessmodespeechinesstempotime_signaturevalencelabel
7400.2020000.7552164500.5770.00000030.2340-7.54910.1570100.03440.4510
7410.1620000.7103091730.7840.00062300.1940-6.86510.0416126.65640.9011
7420.1950000.8872317330.6820.05030040.0623-7.79510.0436115.70040.9621
7430.1250000.4801100750.5600.00000010.1360-7.86810.3510204.16240.5711
7440.1240000.5192422270.4950.00626060.1830-11.00200.101095.07840.2621
7450.0001750.3743338270.9430.00015660.1250-4.10800.0556112.08440.3380
7460.0019700.4872130000.8670.006020100.0968-3.29300.0543160.04840.4030
7470.9160000.6051258670.3140.00000000.3590-7.63110.0327138.14840.8361
7480.1680000.7002494930.8230.00002830.1220-6.89210.0373144.06040.7451
7490.0155000.4772767200.7760.00314010.2030-5.05610.0349131.00440.4291

Duplicate rows

Most frequently occurring

acousticnessdanceabilitydurationenergyinstrumentalnesskeylivenessloudnessmodespeechinesstempotime_signaturevalencelabel# duplicates
60.1660000.7082134400.6660.00022920.0929-7.04210.034989.01940.83413
80.5860000.5652389330.4610.00000000.1620-7.27310.1410157.89440.19913
00.0009860.5781880130.8250.00000010.1760-6.10710.3220130.08940.28302
10.0021300.7332935430.5430.00016910.0703-10.00210.0445106.01940.11802
20.0058700.8252206270.8320.00078950.1140-5.85300.0403122.02140.71312
30.0254000.5412052000.8540.00012520.6510-6.19610.155086.04440.45402
40.0469000.3112084670.3250.00000020.1390-9.04210.028365.09010.66812
50.1370000.6662119310.9480.000000100.1920-2.77610.0638100.99640.52302
70.1820000.8742162480.7060.00000010.3340-5.13210.207089.96840.89502
90.8490000.3901846670.3020.00019100.1220-10.36210.0379109.39430.23212